# popular-strategies engine: 5m bars, 8 popular strategies (PREREG), regime labels. Reuses buyer-method-audit lib/lib_trail (sim, costs, option panels).
import sys; sys.path.insert(0,'/workspace/research/buyer-method-audit/scripts')
from lib_trail import *
L='/workspace/research/popular-strategies'
def bars5(a):
    o,h,l,c,v=a; n=72; B=np.full((5,n),np.nan)
    for k in range(n):
        s=slice(5*k,5*k+5); cc=c[s]
        if np.all(np.isnan(cc)): continue
        B[0,k]=o[5*k] if not np.isnan(o[5*k]) else np.nanmin(o[s]) if not np.all(np.isnan(o[s])) else np.nan
        B[1,k]=np.nanmax(h[s]); B[2,k]=np.nanmin(l[s]); idx=np.where(~np.isnan(cc))[0]; B[3,k]=cc[idx[-1]]; B[4,k]=5*k+idx[-1]
    return B
def km(hm): return (hm2i(hm)-4)//5   # 5m bar index whose last minute is hm
def wilder(x,n):
    out=np.full(len(x),np.nan); s=None; cnt=0; acc=0
    for i,v in enumerate(x):
        if np.isnan(v): out[i]=s if s is not None else np.nan; continue
        if s is None:
            acc+=v; cnt+=1
            if cnt==n: s=acc/n
        else: s=(s*(n-1)+v)/n
        out[i]=s if s is not None else np.nan
    return out
def ema(x,n):
    out=np.full(len(x),np.nan); a=2/(n+1); s=None
    for i,v in enumerate(x):
        if np.isnan(v): out[i]=s if s is not None else np.nan; continue
        s=v if s is None else a*v+(1-a)*s; out[i]=s
    return out
def continuous(S):
    days=sorted(S); rows=[]
    for d in days:
        B=bars5(S[d])
        for k in range(72):
            if not np.isnan(B[3,k]): rows.append((d,k,B[0,k],B[1,k],B[2,k],B[3,k],int(B[4,k])))
    F=pd.DataFrame(rows,columns=['date','k','o','h','l','c','m'])
    c=F.c.values; h=F.h.values; l=F.l.values; pc=np.r_[np.nan,c[:-1]]
    tr=np.nanmax(np.vstack([h-l,np.abs(h-pc),np.abs(l-pc)]),axis=0); atr=wilder(tr,10)
    hl2=(h+l)/2; ub=hl2+3*atr; lb=hl2-3*atr; fub=np.full(len(c),np.nan); flb=np.full(len(c),np.nan); st=np.full(len(c),np.nan); dirn=np.zeros(len(c))
    for i in range(len(c)):
        if np.isnan(atr[i]): continue
        if i==0 or np.isnan(fub[i-1]): fub[i]=ub[i]; flb[i]=lb[i]; dirn[i]=1; st[i]=flb[i]; continue
        fub[i]=ub[i] if (ub[i]<fub[i-1] or c[i-1]>fub[i-1]) else fub[i-1]
        flb[i]=lb[i] if (lb[i]>flb[i-1] or c[i-1]<flb[i-1]) else flb[i-1]
        if dirn[i-1]==1: dirn[i]=-1 if c[i]<flb[i] else 1
        else: dirn[i]=1 if c[i]>fub[i] else -1
        st[i]=flb[i] if dirn[i]==1 else fub[i]
    F['st']=st; F['stdir']=dirn; F['e9']=ema(c,9); F['e21']=ema(c,21)
    d_=np.diff(c,prepend=np.nan); up=np.where(d_>0,d_,0.0); dn=np.where(d_<0,-d_,0.0); up[0]=dn[0]=np.nan
    au=wilder(up,14); ad=wilder(dn,14); F['rsi']=100-100/(1+au/np.where(ad==0,np.nan,ad)); F.loc[(ad==0)&(au>0),'rsi']=100
    return F
def daily_table(S,PC):
    rows=[]
    for d in sorted(S):
        o,h,l,c,v=S[d]
        if np.isnan(o[0]): continue
        rows.append(dict(date=d,open=o[0],high=np.nanmax(h[:360]),low=np.nanmin(l[:360]),prev_close=PC.get(d,np.nan)))
    return pd.DataFrame(rows).set_index('date')
def signals(S,PC):
    F=continuous(S); F['pos']=np.arange(len(F)); G={d:g.set_index('k') for d,g in F.groupby('date')}
    D=daily_table(S,PC); days=list(D.index)
    # prior-day H/L/C (official close) for PDH/PDL and CPR; prior-day high/low from 1m bars 09:15-15:14 (+official close)
    W=[]; out=[]
    for j,d in enumerate(days):
        a=S[d]; o,h,l,c,v=a; vw=vwap(a); g=G.get(d)
        if g is None or j==0: continue
        pd_=days[j-1]; PH,PL,PCl=D.high[pd_],D.low[pd_],D.prev_close[d]
        P=(PH+PL+PCl)/3; BC=(PH+PL)/2; TC=2*P-BC; wid=abs(TC-BC)/P; W.append(wid)
        def first(cond_fn,k0,k1):
            for k in range(k0,k1+1):
                if k not in g.index: continue
                r=cond_fn(k,g.loc[k])
                if r: return k,r
            return None,None
        def add(name,k,dd,stop):
            m=int(g.loc[k,'m']); out.append(dict(cand=name,date=d,m=m,dir=dd,entry=c[m],stop=stop))
        # P1/P2 ORB
        for name,N,k1 in (('P1',15,km('11:59')),('P2',5,km('10:59'))):
            orh=np.nanmax(h[:N]); orl=np.nanmin(l[:N])
            k,r=first(lambda k,b:(1 if b.c>orh else -1 if b.c<orl else 0),N//5,k1)
            if k is not None: add(name,k,r,orl if r>0 else orh)
        # P3 PDH/PDL
        if PL<o[0]<PH:
            k,r=first(lambda k,b:(1 if b.c>PH else -1 if b.c<PL else 0),1,km('13:59'))
            if k is not None: add('P3',k,r,(g.loc[k,'l']-5) if r>0 else (g.loc[k,'h']+5))
        # P4 narrow CPR
        hist=W[-251:-1]
        if len(hist)>=120 and wid<np.percentile(hist,30):
            top,bot=max(TC,BC),min(TC,BC)
            k,r=first(lambda k,b:(1 if (b.c>top and b.c>vw[int(b.m)]) else -1 if (b.c<bot and b.c<vw[int(b.m)]) else 0),3,km('11:59'))
            if k is not None: add('P4',k,r,bot if r>0 else top)
        # P5 Supertrend flip + VWAP
        idx=list(g.index); 
        def prevrow(k):
            i=int(g.loc[k,'pos']); return F.iloc[i-1] if i>0 else None
        for k in range(km('09:44'),km('14:29')+1):
            if k not in g.index: continue
            b=g.loc[k]; pr=prevrow(k)
            if pr is None: continue
            if b.stdir==1 and pr.stdir==-1 and b.c>vw[int(b.m)]: add('P5',k,1,b.st); break
            if b.stdir==-1 and pr.stdir==1 and b.c<vw[int(b.m)]: add('P5',k,-1,b.st); break
        for k in range(km('09:44'),km('14:29')+1):
            if k not in g.index: continue
            b=g.loc[k]; pr=prevrow(k)
            if pr is None: continue
            i=int(g.loc[k,'pos']); last6=F.iloc[max(0,i-5):i+1]
            if b.e9>b.e21 and pr.e9<=pr.e21 and b.c>vw[int(b.m)]: add('P6',k,1,last6.l.min()-5); break
            if b.e9<b.e21 and pr.e9>=pr.e21 and b.c<vw[int(b.m)]: add('P6',k,-1,last6.h.max()+5); break
        for k in range(km('09:44'),km('14:29')+1):
            if k not in g.index: continue
            b=g.loc[k]; pr=prevrow(k)
            if pr is None or np.isnan(pr.rsi) or np.isnan(b.rsi): continue
            i=int(g.loc[k,'pos']); last6=F.iloc[max(0,i-5):i+1]
            if pr.rsi<30<=b.rsi: add('P7',k,1,last6.l.min()-5); break
            if pr.rsi>70>=b.rsi: add('P7',k,-1,last6.h.max()+5); break
        # P8 gap fade
        gap=(o[0]-PCl)/PCl if PCl==PCl else np.nan
        if gap==gap and 0.003<=abs(gap)<=0.015:
            orh=np.nanmax(h[:15]); orl=np.nanmin(l[:15]); dd=-1 if gap>0 else 1
            k,r=first(lambda k,b:(dd if ((dd<0 and b.c<orl) or (dd>0 and b.c>orh)) else 0),3,km('10:59'))
            if k is not None: add('P8',k,r,orh if r<0 else orl)
    X=pd.DataFrame(out)
    X[['stop','R']]=[clipstop(e,s,dd) for e,s,dd in zip(X.entry,X.stop,X.dir)]
    return X